Population attributable risk for colorectal and breast cancer in England, Wales, Scotland, Northern Ireland, and the United Kingdom

Background: The population attributable risk (PAR) is a statistic commonly used for quantifying preventability of cancer. We report here PAR estimates for the United Kingdom (UK) along with its constituent countries for up-to-date risk factor-attributable colorectal cancer (CRC) and breast cancer (BC), focusing on diet and nutrition related factors and tobacco (CRC) using representative national surveys. Methods: The PAR was calculated using established, modifiable risk factors by the World Cancer Research Fund/American Institute of Cancer Research (WCRF/AICR): physical activity, body mass index (BMI), alcoholic drinks, red meat, processed meat, dietary fiber, dietary calcium, as well as cigarette smoking for CRC, and physical activity, BMI, alcoholic drinks, and fruits and vegetable consumption for BC. National prevalence estimates and relative risks (RRs) for CRC and BC were obtained from meta-analyses or large pooled analyses. Results: Based on eight dietary and lifestyle risk factors, the estimates for attributable cases of CRC for males and females, respectively, were as follows: England: 67% and 60%; Scotland: 68% and 59%, Wales: 66% and 61%; Northern Ireland: 67% and 61%; and UK: 67% and 60%. Excluding smoking, the PAR for the UK was 61% for men and 52% for women. Based on four dietary and lifestyle risk factors, the estimates for BC were as follows: England: 26%, Scotland: 27%; Wales: 25%; Northern Ireland: 26%; and UK: 27%. Conclusion: Up to 67% for CRC and 27% of BC were attributable to modifiable dietary and lifestyle factors in the UK. Moderate differences in PAR are observed between countries due to different prevalence of exposure to risk factors.


Introduction
The population attributable risk (PAR) is a metric commonly utilized to quantify the preventability of a specific disease.Various approaches to calculate the PAR for cancer have been used by researchers.Most typically, the PAR is based on identifying relative risks (RRs) from the scientific literature and prevalence from suitable population sources for specific factors of interest.The process requires several steps.It is critical to first identify the established risk factors that are in principle modifiable.Then it is important to derive a RR estimate from the literature for each risk factor.The RR estimates can either be from the population of interest, or common RRs, such as from meta-analyses or representative studies, that are generalizable.Next, it is important to identify estimates of the prevalence of exposures in the population of interest.With this information, standard formulae to calculate PARs can be used.
Estimates of the PAR for various cancers have often varied widely.Four decades ago, Doll and Peto 1 suggested that 90% of colorectal cancers (CRCs) and 50% of breast cancers (BCs) may be related to diet.Similarly, Parkin et al. 2 attributed about 43% of CRC in the UK to five largely modifiable factors including diet and nutrition, and 42% of BC to modifiable risk factors including body fatness and physical activity.PAR estimates for cancer from studies across the world have varied substantially; for example, some estimates were between 16% and 90% for CRC [3][4][5] , and 6.5% and 50% for BC [1][2][3]6 . Blt and Tarone 7 argued that an estimate of 90% appeared to be too high for CRC.Such variability in the calculated PAR estimates may extend from a number of factors 8 including: 1) the risk factors that were considered; 2) the specific RRs that were utilized; 3) the sources of population prevalence of the risk factors; and 4) the specific calculation methods used to calculate the PAR.Other contributors to variability include variation in the time period and geography, differences in socio-demographic profile of cancer cases, and differences due to screening availability.
We report here PAR estimates for the United Kingdom (UK) along with its constituent countries on the number of risk factor-attributable CRC and BC, the two most common preventable cancers, excluding lung cancer, which is highly related to tobacco use.We focused on diet-and nutrition-related factors (and tobacco for CRC) based on up-to-date criteria from the World Cancer Research Fund/American Institute of Cancer Research (WCRF/AICR) 9 .We used representative national surveys for risk factor prevalence in individual UK countries given that the risk factor prevalence varies 10 .Hence, we estimated the PARs for CRC and BC in the UK, and separately, England, Scotland, Wales, and Northern Ireland.

Factor selection
Based on criteria from the WCRF/AICR 9 , we used only factors classified as having achieved a level of evidence for a causal association as "probable" or "convincing" (displayed in Table 1).This level of evidence is considered "actionable" by the WCRF/AICR.In addition to the seven factors that met this classification for CRC (body mass index (BMI), physical activity, alcoholic drinks, and intakes of dietary fiber, red meat, processed meat, and dietary calcium), we further included cigarette smoking, considered a causal factor for CRC based on the United States Surgeon General Report 11 .We included four factors for BC (body fatness/BMI, physical activity, alcoholic drinks, and intakes of fruits and vegetables).We included "fruits and vegetables" because, although these were considered a limited/suggestive factor for BC, the WCRF/AICR expert panel concluded that that evidence for carotenoid rich foods was stronger for estrogen receptor negative BC than for estrogen receptor positive or total BC.Yet, because estrogen receptor negative BC is an important component of total BC, preventing it would contribute to reducing the burden of BC.

Prevalence data
The prevalence of exposures to risk factors in the populations was obtained from the nationally representative population surveys: the Health Survey of England, Scotland, Wales, and Northern Ireland 12 .Exposure distribution data on dietary calcium and meat consumption were obtained from EPIC-Oxford cohort 13 and UK biobank cohort 14 , respectively.The methods detailing the derivation of prevalence for most exposures have been described in Brown et al. 12 .

Relative risks
We identified RRs by conducting systematic searches in PubMed.Meta-analyses of cohort studies were the preferred source of RRs, then followed by pooled analyses of cohort studies and individual cohort studies.In some meta-and pooled analyses multiple estimates were reported, and sometimes more than one meta-or pooled analysis was available; in these circumstances, we selected the RRs based on characteristics most relevant to our study.In addition, selected RRs had to provide cut-points for the categories comparable to the exposure data available for UK and its constituents countries.The search string for the risk factors were: Tobacco (tobacco OR cigarette OR smoking OR environmental tobacco smoke OR second-hand smoke), Overweight and obesity (weight OR BMI OR body mass index OR obesity OR obese OR overweight OR adiposity OR body size), Alcohol (alcohol OR alcoholic OR ethanol), Fiber (fibre OR fiber), Processed and red meat (Meat OR bacon OR ham OR sausages OR jerky OR   MET-m/week 8 .We then computed an average RR for the first two categories representing: achieving less than 600 METs-m/week or active less than 5 days per week/ not achieving 30 minutes of physical activity on 5 days per week and active more than 5 days per week 8 , that matches well with the exposure prevalence data on the UK national health surveys (prevalence of physical activity was provided as days per week on which at least 30 minutes of moderate physical activity was completed).For physical activity and CRC, we took a weighted average of colon and rectal cancers (70% colon cancer and 30% rectal cancer 15 ) to calculate the RR.Such assumptions made for RRs could have resulted in some small differences in the results.

Exposure
For the factors associated with increased risk (BMI, alcohol, red meat, processed meat), we used the lowest category as the reference group.For the protective factors (physical activity, fiber, fruits and vegetable, and calcium), we chose the highest category as the reference group, and PAR was calculated using the reciprocal of the RR.For studies that provided a linear dose-response relationship, the RRs were first transformed into a log scale, divided by the value, then exponentiated.

Statistical analysis
For each of the risk factors, we identified n levels of exposure categories.We then estimated PAR using the following equation: ( 1) 1 where P i is the exposure prevalence at the exposure category i and RR i is the corresponding RR of CRC or BC at exposure category i.The details for the categorizations of exposures are presented in Table 1.
We then estimated the preventability of CRC or BC that was attributable to the combined dietary and lifestyle risk factors the following equation: where i signifies the level of individual risk factors (i = 1,…, n).
For fiber intake, the PAR was directly obtained from Brown et al. because our estimate was based on the same prevalence data and RR 12 .The PAR estimates were directly computed manually using the formulae.

Sensitivity analysis
We conducted an additional analysis where we exclude probable factors (defined by WCRF/AICR) and kept only the convincing factors from the calculation of the PAR.For CRC, the three probable factors (red meat, dietary fiber, and dietary calcium) were excluded from the analysis.We calculated the proportion of BC attributable to lifestyle factors excluding suggestive (fruits and vegetable consumption) and probable factors (physical activity), resulting in these two factors: body fatness/BMI and alcoholic drinks.

Results
The proportion of CRC cases attributable to lifestyle risk factors for the UK constituents' countries were estimated as follows: 67% for British males and 60% for British females, 68% for Scottish males and 59% for Scottish females, 66% for Welsh males and 61% for Welsh females, 67% for Northern Irish males and 61% for Northern Irish females, and 67% for UK males and 60% for UK females, overall (Table 2).The proportion of CRC cases attributable to lifestyle risk factors excluding cigarette smoking were 62% for British males and 53% for British females, 62% for Scottish males and 51% for Scottish females, 61% for Welsh males and 52% for Welsh females, 62% for Northern Irish males and 52% for Northern Irish females, and 61% for UK males and 52% for UK females (Table 2).The intake of dietary fiber was the major contributor to the attributable CRC cases, accounting for 25% for males and 32% for females in the UK, followed by processed meat intake (14% for men and 10% for women).For alcoholic drinks, the PAR values were substantially higher for men than for women.
The proportions of BC cases attributable to lifestyle risk factors were 26% for British women, 27% for Scottish, 25% for Welsh, 26% for Northern Irish, and 27% for UK women, overall (Table 2).Alcohol was the largest contributor to the estimated attributable BC cases, accounting for 8% for females in UK, followed by the body fatness (7.6%) and insufficient physical activity (7%).
The estimates for the prevalence and the RRs for the various exposures used in our calculation are presented in Table 3 and Table 4.

Sensitivity analysis
The PARs for CRC based on the five "convincing" factors (body fatness/BMI, physical activity, alcohol, processed meat, and cigarette smoking) were 48% for UK males and 34% for UK females, and excluding smoking, these were 40% and 21%, respectively (Table 5).After excluding the "probable" factors for BC, the PARs including the 2 factors (body fatness/BMI and alcoholic drinks) were 15% for UK females (Table 5).

Discussion
We provided PAR estimates for the UK and its constituent country-level for diet and lifestyle risk factors where evidence for a causal role in CRC and BC development is probable/convincing based on WCRF/AICR systematic reviews.We estimated that 67% of CRC cases in men and 60% of cases for women in the UK, and 27% of BC cases were attributable to dietary and lifestyle risk factors assessed in adulthood.
Excluding smoking from the calculation, these estimates for CRC were 61% for males and 52% for females.Significant differences in the CRC PAR by sex were observed for body fatness, alcohol, and fiber intake; these results were mostly driven by sex differences in the RR estimates, and partly by higher levels of alcohol drinking in men.Moderate differences in the PAR estimates were observed between countries due to different prevalence of exposure to risk factors.For instance, prevalence of obesity/body fatness was slightly lower in Wales compared to other countries in the UK, resulting in slightly lower PAR estimates of both CRC and BC for Wales overall.
except for dietary calcium and meat.Exposure distribution data on dietary calcium was obtained from the EPIC-oxford cohort 13 .Exposure distribution data on meat consumption was obtained from the UK biobank cohort 14 .
Previous studies have considered the PAR in the UK and its individual countries.The results we report here show overall consistency with those from similar studies, despite some differences in the risk factors considered, the time periods encompassed, and the RR sources.In one study to determine preventability in the UK (2018) adults aged 30 years and older, the PAR of CRC attributable to tobacco smoking, alcohol, intakes of meat and fiber, overweight and obesity, physical exercise, and ionizing radiation was 57% for men and 51% for women 12 .In the same study, the PAR of BC attributable to alcohol drinking, intakes of fruits and vegetables, overweight and obesity was 27% in 2010 2 and 23% in 2018 12 .These results were similar but slightly lower compared with our estimates.This other analysis 12 did not account for red meat or dietary calcium, considered as probable risk factors by WCRF/AICR, and included radiation and oral contraceptive use, which we did not consider as our analysis focused on the most up-to-date evidence from the WCRF/AICR for modifiable cancers under the domain of diet and nutrition.Wide variations in PAR estimates were observed in some previous studies across the world.For example, in a study that examined overweight and obesity, physical inactivity, and low consumption of fruits and vegetables in relation to CRC risk worldwide, the estimated PAR was 13% 25 .A report of alcohol, obesity and overweight, and physical inactivity as modifiable risk factors associated with CRC risk found that the PAR was 19% for the French population, and 21% for men and 16% for women 5 .Although our estimates were for the UK rather than worldwide, they suggest that the PAR of 13% probably underestimates the true preventability because the overall CRC rates are not markedly different between France and the UK.
Our estimates of the PAR here were based on our judgement of the best available and most relevant data, and thus cautious interpretation is warranted.These limitations could bias either toward underestimation or overestimation of PARs.We have previously shown that the choice of risk factors and selection criteria for the sources of RRs could influence the PAR estimates, although in general, the methods appear relatively robust 8 .We further performed sensitivity analysis by excluding probable factors to assess the influence of risk factor selection on PAR estimation.Yet this analysis is likely to considerably underestimate the true PAR because the strength of evidence for "probable" risk factors is high enough to be considered actionable by the WCRF/AICR.The associations classified as "probable" by the WCRF/AICR are robust, but, nevertheless, because they are based on observational studies, confounding cannot be discounted with certainty.Another limitation is, even if the considered factors are truly causal, it is unclear when in life they need to be altered to mitigate risk.
On the other hand, some considerations indicate that we may underestimate the true preventability.Most studies utilize a single measure based on a dietary or physical activity assessment with considerable measurement error; thus, any true associations would tend to be underestimated because random error typically causes a true association to tend towards the null value.Although BMI is a useful measure and generally measured well, it is not a perfect measure of the most relevant component of adiposity (e.g., visceral fat).The PARs for alcohol are prone to be underestimates because non-drinkers (the presumed low-risk group) may include past drinkers who may have consumed heavily before stopping to drink 19 .Finally, the overall preventability over the life course is likely to be underestimated because we only considered adulthood diet.In fact, adolescence is emerging as an important time period of increased susceptibility to carcinogenic exposures, including diet, especially for BC but also for CRC.
In conclusion, our study reported the PAR of CRC and BC attributable to modifiable risk factors in England, Scotland, Wales, Northern Ireland, and UK.Up to 67% for CRC and 27% of BC were attributable to known established modifiable risk factors in adulthood in the UK.These results reinforce the importance for diet and lifestyle for the prevention of major cancers in the UK.
with its constituent countries for up-to-date risk factor-attributable colorectal cancer (CRC) and breast cancer (BC), focusing on diet and nutrition related factors and tobacco (CRC) using representative national surveys.
The manuscript is well-written and nicely presented, with a good balance of descriptive text and speech and practical tables of the use of the package.
The methods used seem relatively robust but limited in their causality, they did not include incidence, although the study provides interesting data on the two important neoplasms in public health.However, it was limited to estimates of national prevalence only and the relative risks (RR) failed to include important socio-demographic risk factors such as age group for both CRC and CC.
Discussion.The limitations of the study were mentioned, which are cautious in making strong statements regarding the results.
An important question would be about the validity of the study, the text does not say anything and about the robustness of the study, or if the research question was important and if the study it was original.

Is the work clearly and accurately presented and does it cite the current literature? Yes
Is the study design appropriate and is the work technically sound?Partly

Are sufficient details of methods and analysis provided to allow replication by others? Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable
Are all the source data underlying the results available to ensure full reproducibility?Yes

Are the conclusions drawn adequately supported by the results? Yes
Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Epidemiology
I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
Reviewer Report 20 July 2021 https://doi.org/10.21956/amrcopenres.14053.r26735 calculates RR based on per unit RR, which is a usual practice in this field.Then for dietary calcium, they calculated an average RR for 0-199 mg/day, 200-399 mg/day, and 400-599 mg/day from their IJC paper (Kim H. et al., Int J Cancer, 2021, 148(12):2947-53 3 ) to match 0-524 mg/day of the lowest category of exposure in this study (page 5, first paragraph).It was unclear to me how they averaged the RR and how they resolved the issue that the highest category (400-599mg/day) exceeds the upper limit (524 mg/day).Why not just take the midpoint value of this category (262 mg/day) and combine it with the risk per unit value?For example, for a reference calcium level of 1000 mg/day and per mg/day unit RR of r_mg, the RR for the category of 0-524 mg/day is r_mg^(1000-262).Some other concerns: The authors used a formula, exp(lnX/A *B) to calculate RR, while neither A nor B was defined.The authors referenced their recent publication in IJC for this formula (Kim H. et al., Int J Cancer, 2021, 148( 12):2947-53 3 ).But in their IJC paper, the formula was laid out as "RR_B = exp(log(RR_B)/A *B)", which is simply wrong."log(RR_B)" should be "log(RR_A)".The authors should write an erratum to IJC to have it corrected. 1.
In the second paragraph of the Method section, the author stated, "the prevalence of exposures to risk factors in the populations was obtained from the nationally representative population surveys" (page 3).However, which risk factors exactly were not stated.This caused some confusions to me, as I tried to figure out from which data source they obtained the prevalence of processed meat consumption.

2.
In Table 2, there is no variation in PAR by countries for red meat and dietary calcium (page 6).It is unclear whether there is indeed no country-level variation for these two risk factors, or there were no prevalence data to support this analysis, or there was a data-entry mistake.The authors didn't have any discussions around it.If there were no prevalence data to support this analysis, then the discussion around differences in PAR between countries should acknowledge this limitation.

3.
At the end of the second paragraph of introduction (page 3), the authors stated that other contributors to the variability in calculated PAR estimates by different studies differences include "socio-demographic profile of cancer cases, and differences due to screening availability".As the authors didn't provide citations to support this statement, could the authors further elaborate on how these two factors contribute to the variability?In particular, it is difficult to fathom how screening availability, a factor related to the secondary intervention of CRC and BC, contributes to the PAR of diet and nutrition.

4.
The authors used an equation that has been widely adopted for estimating combined risk factors (page 5, paragraph 4).However, many researchers used this formula without disclaiming the two important assumptions associated with it (Steenland and Armstrong,  2006, Epidemiology, 17(5):512-9 4 ): independent risk exposure distributions and no statistical interactions between any two risks.These two assumptions should not only be explicitly stated in the method section, but also be worthy of several sentences in the discussion section.Also on page 5 after the formula, the author said "where i signifies the level of individual categories (i = 1,…, n)." "Categories" here should be "risk factors".Again for this paragraph, the first sentence "We then estimated the preventability of CRC or BC that was attributable to the combined dietary and lifestyle risk factors the following equation" is missing a preposition.

Is the work clearly and accurately presented and does it cite the current literature? Partly
Is the study design appropriate and is the work technically sound?No

Are sufficient details of methods and analysis provided to allow replication by others? Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable
Are all the source data underlying the results available to ensure full reproducibility?Yes

Are the conclusions drawn adequately supported by the results? Partly
Competing Interests: No competing interests were disclosed.
I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.
PAR (the latter of which I suspect is what the authors did).This is because some risk factors are more prevalent among older age groups (e.g., physical inactivity, body fatness) and most cancer incidences are disproportionally higher among older age groups.Second, the authors had to assume a weighted average of 70% colon and 30% rectal cancers for physical activity and CRC (page 5), which wouldn't be necessary if they had the incidence data by colon and rectum.Author's response: The relative risk estimates are based on the literature and not given in age stratification typically.The method relies on two parameters, the relative risk and the prevalence of the exposure.The proportion is what is calculated, not the absolute number of cancers estimated.Of course, the proportion can be multiplied by the incidence to get the absolute numbers.The 70-30 assumption was made based on the paper was based on the RR estimated for colon cancer is 0.84 and that for rectal cancer 0.87.We assumed that for colorectal cancer, the RR would be a weighted average of these, which is around 0.85 (i.e., if the RR for colon cancer is 0.84 and that for rectal cancer around 0.87, the RR for CRC would be around 0.85).Because these values are so close anyway, this assumption appears reasonable.The 70-30 prevalence was assumed based on published data.(Siegel RL, Miller KD, Goding Sauer A, Fedewa SA, Butterly LF, Anderson JC, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2020).
The authors didn't correctly estimate the PAR of body fatness and physical activity for BC.These two risk factors are only associated with postmenopausal breast cancers, whereas the authors included premenopausal breast cancers as well. 1.
Author's response: Incidence data do not distinguish pre-and post-menopausal breast cancer, so these cannot be separated directly.Physical activity is considered a (protective) risk factor by the WCRF/AICR for both pre-and post-menopausal breast cancer.The only factor that is different is body fatness, which is considered a risk factor only for postmenopausal women.Assuming that body fatness is only a risk factor for postmenopausal breast cancer, they account for about 75% of cases.Since one of the four factors is not relevant for 25% of the cases, our estimate may be overestimated by around 5%.
The way the authors calculated RRs for calcium and physical activity is difficult to understand.They used a formula that assumes a loglinear dose-response relationship and calculates RR based on per unit RR, which is a usual practice in this field.Then for dietary calcium, they calculated an average RR for 0-199 mg/day, 200-399 mg/day, and 400-599 mg/day from their IJC paper (Kim H. et al., Int J Cancer, 2021, 148(12):2947-53 3 ) to match 0-524 mg/day of the lowest category of exposure in this study (page 5, first paragraph).It was unclear to me how they averaged the RR and how they resolved the issue that the highest category (400-599mg/day) exceeds the upper limit (524 mg/day).Why not just take the midpoint value of this category (262 mg/day) and combine it with the risk per unit value?For example, for a reference calcium level of 1000 mg/day and per mg/day unit RR of r_mg, the RR for the category of 0-524 mg/day is r_mg^(1000-262). 1.
Author's response: Because of the exposure data unavailability that matches with the RRwe had to average RR to match with a specific category of intake.Exposure data wasn't specified in the cohort we used data from the way we categorized to match with RR.Some other concerns: The authors used a formula, exp(lnX/A *B) to calculate RR, while neither A nor B was 1.
defined.The authors referenced their recent publication in IJC for this formula (Kim H.  et al., Int J Cancer, 2021, 148(12):2947-53 3 ).But in their IJC paper, the formula was laid out as "RR_B = exp(log(RR_B)/A *B)", which is simply wrong."log(RR_B)" should be "log(RR_A)".The authors should write an erratum to IJC to have it corrected.Author's response: This was a typo in the PMC version.Now it is fixed.
In the second paragraph of the Method section, the author stated, "the prevalence of exposures to risk factors in the populations was obtained from the nationally representative population surveys" (page 3).However, which risk factors exactly were not stated.This caused some confusion to me, as I tried to figure out from which data source they obtained the prevalence of processed meat consumption.

1.
Author's response: Meat and calcium were not from the nationally representative survey, data sources were mentioned in the table as a footnote.
In Table 2, there is no variation in PAR by countries for red meat and dietary calcium (page 6).It is unclear whether there is indeed no country-level variation for these two risk factors, or there were no prevalence data to support this analysis, or there was a data-entry mistake.The authors didn't have any discussions around it.If there were no prevalence data to support this analysis, then the discussion around differences in PAR between countries should acknowledge this limitation.

1.
Author's response: Prevalence data were only for the UK-not for country-specific.
At the end of the second paragraph of the introduction (page 3), the authors stated that other contributors to the variability in calculated PAR estimates by different studies differences include "socio-demographic profile of cancer cases and differences due to screening availability".As the authors didn't provide citations to support this statement, could the authors further elaborate on how these two factors contribute to the variability?In particular, it is difficult to fathom how screening availability, a factor related to the secondary intervention of CRC and BC, contributes to the PAR of diet and nutrition.

1.
Author's response: Screening is typically associated with the socio-demographic profile, and thus often with the dietary and lifestyle factors that we assessed.The broad effect of colonoscopy is to lower colorectal cancer risk (by removing precursors) and mammography tends to lead to more cases diagnosed ("overdiagnosis").How these affect our results is complex; for example, they may affect the initial RR estimates (confounding).
The authors used an equation that has been widely adopted for estimating combined risk factors (page 5, paragraph 4).However, many researchers used this formula without disclaiming the two important assumptions associated with it (Steenland and  Armstrong, 2006, Epidemiology, 17(5):512-9 4 ): independent risk exposure distributions and no statistical interactions between any two risks.These two assumptions should not only be explicitly stated in the method section but also be worthy of several sentences in the discussion section.Also on page 5 after the formula, the author said "where i signifies the level of individual categories (i = 1,…, n)." "Categories" here should be "risk factors".Again for this paragraph, the first sentence "We then estimated the preventability of CRC or BC that was attributable to the combined dietary and lifestyle risk factors the following equation" is missing a 1.
preposition.Author's response: We agree that these are important assumptions for the formula.It has been fixed in the revised one.Page 5 line 10, "600 METs-hours/week" should be "600 MET-m/week".(fixed) 1.
In summary, I find little value being added by this paper beyond the study by Brown  et al. (2018).The authors should justify the validity of their PAR estimates for dietary calcium and red meat and address all my concerns above before the paper could be considered having reasonable quality.Author's response: Because nationally representative exposure data were not available for meat and calcium -we had to use a large UK-based cohort (Biobank and EPIC).This may have caused a slight underestimate in the PAR estimate.

2.
Competing Interests: No competing interests were disclosed.
than 5 days of at least 30 minutes activity per week or < 600 MET-m/week Active, more than 5 days of at least 30 minutes of activity or ≥ 600 MET-m/week Colon (C18) Processed meat < 25 g/day vs. ≥ 25 g/day Colorectum (C18-C20) Red meat < 70g/day vs. ≥ 70 g/day Colorectum (C18-C20) than 5 days of at least 30 minutes activity per week or < 600 MET-m/week Active, more than 5 days of at least 30 minutes of activity or ≥

Table 3 . Distribution of colorectal cancer and breast cancer exposures in the UK by country and sex*.
These estimates were prevalence calculated by Brown et.al.2015 12 .Exposure distribution data was obtained from the Health survey of England, Scotland, Wales, and Northern Ireland, and National Diet and Nutrition Survey *

Table 5 . Additional analysis: Preventability estimates (PAR) for CRC and BC in the UK, excluding probable factors based on the WCRF/AICR.
*Exposure categories same as Table1